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master_cate_fi6

Text Classification
setfit
Safetensors
sentence-transformers
roberta
generated_from_setfit_trainer
Eval Results
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master_cate_fi6 / README.md
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tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 탑키드 만들기 경찰관 놀이 세트 3인용 가구/인테리어>수예>기타수예
- text: 일상공방 코 손뜨개 6종세트 인디핑크 421114 가구/인테리어>수예>뜨개질>완제품
- text: 퀼트가게6마 반폭롤 면 100 20수 도기 프렌즈 WS 792 원단 가구/인테리어>수예>퀼트/펠트>원단
- text: 펠트 구절초 대 SET 환경꾸미기재료 가구/인테리어>수예>퀼트/펠트>도안
- text: 광목침구 촬영용 빈티지 플라워 코튼 포플린 드레스 셔츠 섬유 린넨 대폭원단 가구/인테리어>수예>자수>원단
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: mini1013/master_domain
model-index:
- name: SetFit with mini1013/master_domain
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 1.0
name: Accuracy
---
# SetFit with mini1013/master_domain
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 7 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6.0 | <ul><li>'퀼트크로스백 퀼트완제품 가구/인테리어>수예>퀼트/펠트>완제품'</li><li>'현진 글리터토퍼 꽃길만걷자 GFT4-405 152094 가구/인테리어>수예>퀼트/펠트>완제품'</li><li>'스위티퀼트 퀼트 완제품 봄이 필통 파우치 가구/인테리어>수예>퀼트/펠트>완제품'</li></ul> |
| 4.0 | <ul><li>'쇼파 빈티지 요곤 가죽 질감 재킷 배경 가방 부드러운 안감천 레자원단 가구/인테리어>수예>원단'</li><li>'접착 레자 소파 고무 강력 자동차 인테리 인조 가죽 가구/인테리어>수예>원단'</li><li>'핸드메이드 가죽 소재 하운드투스 Y자 인조 PVC클러치 프린트 캐리어 DIY 가구/인테리어>수예>원단'</li></ul> |
| 3.0 | <ul><li>'실밥뜯개 실뜯게 제거기 부자재 니퍼 실따개 바느질 마대바늘 모루인형눈 스킬바늘 재단가위 가구/인테리어>수예>수예용품/부자재'</li><li>'diy 가죽공예 세트 왁스실 가죽바늘 7종 가구/인테리어>수예>수예용품/부자재'</li><li>'단추 썬그립 500세트 똑딱이단추 고급 국산 티단추 스냅 선그립 79컬러 가구/인테리어>수예>수예용품/부자재'</li></ul> |
| 1.0 | <ul><li>'도서 다루마 패턴북 6 가구/인테리어>수예>뜨개질>패키지'</li><li>'뜨개가방손잡이 우드 자연 큰 단단한 나무 잠금 가구/인테리어>수예>뜨개질>완제품'</li><li>'타월 담요 소파 손뜨개 여름 블랭킷 커버 코바늘 가구/인테리어>수예>뜨개질>완제품'</li></ul> |
| 0.0 | <ul><li>'우돌아트 동물이름표 기린 네임텍 스텐실 도안 1243 가구/인테리어>수예>기타수예'</li><li>'모루 공예 재료 부드러운 모루 - 초록 가구/인테리어>수예>기타수예'</li><li>'컬러점토 3개입 아모스 가구/인테리어>수예>기타수예'</li></ul> |
| 5.0 | <ul><li>'OOE 덴마크 꽃실 자수실 510 727 가구/인테리어>수예>자수>실/바늘'</li><li>'데코샌드아트 명화도안 색모래 밤의 별매 중 X 2매입 가구/인테리어>수예>자수>도안'</li><li>'실십자수 동물 왕 사자 가족 대형 십자수 세트 패키지 DIY만들기 30x40 11CT HMA56704 가구/인테리어>수예>자수>패키지'</li></ul> |
| 2.0 | <ul><li>'누니액자 보석십자수 패브릭형 액자 60x90 프리미엄 클래식실버 가구/인테리어>수예>보석십자수'</li><li>'돈그림 황금돈나무 거실 현관 행운의 풍수 금전운 그-D 40x80 가구/인테리어>수예>보석십자수'</li><li>'보석십자수 빗 가구/인테리어>수예>보석십자수'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 1.0 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("mini1013/master_cate_fi6")
# Run inference
preds = model("탑키드 만들기 경찰관 놀이 세트 3인용 가구/인테리어>수예>기타수예")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 2 | 8.8714 | 24 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0.0 | 70 |
| 1.0 | 70 |
| 2.0 | 70 |
| 3.0 | 70 |
| 4.0 | 70 |
| 5.0 | 70 |
| 6.0 | 70 |
### Training Hyperparameters
- batch_size: (256, 256)
- num_epochs: (30, 30)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 50
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:----:|:-------------:|:---------------:|
| 0.0104 | 1 | 0.5007 | - |
| 0.5208 | 50 | 0.4969 | - |
| 1.0417 | 100 | 0.4332 | - |
| 1.5625 | 150 | 0.0551 | - |
| 2.0833 | 200 | 0.0001 | - |
| 2.6042 | 250 | 0.0 | - |
| 3.125 | 300 | 0.0 | - |
| 3.6458 | 350 | 0.0 | - |
| 4.1667 | 400 | 0.0 | - |
| 4.6875 | 450 | 0.0 | - |
| 5.2083 | 500 | 0.0 | - |
| 5.7292 | 550 | 0.0 | - |
| 6.25 | 600 | 0.0 | - |
| 6.7708 | 650 | 0.0 | - |
| 7.2917 | 700 | 0.0 | - |
| 7.8125 | 750 | 0.0 | - |
| 8.3333 | 800 | 0.0 | - |
| 8.8542 | 850 | 0.0 | - |
| 9.375 | 900 | 0.0 | - |
| 9.8958 | 950 | 0.0 | - |
| 10.4167 | 1000 | 0.0 | - |
| 10.9375 | 1050 | 0.0 | - |
| 11.4583 | 1100 | 0.0 | - |
| 11.9792 | 1150 | 0.0 | - |
| 12.5 | 1200 | 0.0 | - |
| 13.0208 | 1250 | 0.0 | - |
| 13.5417 | 1300 | 0.0 | - |
| 14.0625 | 1350 | 0.0 | - |
| 14.5833 | 1400 | 0.0 | - |
| 15.1042 | 1450 | 0.0 | - |
| 15.625 | 1500 | 0.0 | - |
| 16.1458 | 1550 | 0.0 | - |
| 16.6667 | 1600 | 0.0 | - |
| 17.1875 | 1650 | 0.0 | - |
| 17.7083 | 1700 | 0.0 | - |
| 18.2292 | 1750 | 0.0 | - |
| 18.75 | 1800 | 0.0 | - |
| 19.2708 | 1850 | 0.0 | - |
| 19.7917 | 1900 | 0.0 | - |
| 20.3125 | 1950 | 0.0 | - |
| 20.8333 | 2000 | 0.0 | - |
| 21.3542 | 2050 | 0.0 | - |
| 21.875 | 2100 | 0.0 | - |
| 22.3958 | 2150 | 0.0 | - |
| 22.9167 | 2200 | 0.0 | - |
| 23.4375 | 2250 | 0.0 | - |
| 23.9583 | 2300 | 0.0 | - |
| 24.4792 | 2350 | 0.0 | - |
| 25.0 | 2400 | 0.0 | - |
| 25.5208 | 2450 | 0.0 | - |
| 26.0417 | 2500 | 0.0 | - |
| 26.5625 | 2550 | 0.0 | - |
| 27.0833 | 2600 | 0.0 | - |
| 27.6042 | 2650 | 0.0 | - |
| 28.125 | 2700 | 0.0 | - |
| 28.6458 | 2750 | 0.0 | - |
| 29.1667 | 2800 | 0.0 | - |
| 29.6875 | 2850 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.2.0a0+81ea7a4
- Datasets: 3.2.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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